In the training phase, we clean and enrich the event log and use it to train predictive and causal models. The predictive model is capable of estimating, for a given ongoing case, the probability that it will end in an undesired outcome (\(\mathbb {P}(uout)\) ). The causal model estimates the effect that an intervention would have if applied to an ongoing case, specifically, the increase in the probability of a positive outcome should the intervention be triggered (a.k.a. the Conditional Average Treatment Effect – \(CATE\) ). The details of how the predictive and causal models are trained are described in our previous work [1]}, [2]}. Below, we summarize this phase to make this paper self-contained.
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